Optimization of the Petroleum Product Supply Chain under Uncertainty: A Case Study in Northern Brazil

A mathematical tri-level programming model for designing an integrated dynamic petroleum product supply chain

Purpose

The purpose of this paper is to design petroleum products’ supply chain management, which includes efficient integration of suppliers, manufacturers, storehouses and retailers.

Design/methodology/approach

This paper proposes that a three-level supply chain will be turned into a bi-level supply chain of petroleum products by simultaneous integration of the middle level with the upstream and downstream levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels.

Findings

The concepts of the design, structure and outputs are led by the model's solution. The model also responds to the variations in the market via coordination in the related decisions to the distribution, production and inventory issues, and also coordinating between the demands and production.

Research limitations/implications

This paper has limited its analysis to definite values due to the over-expansion of calculations and analysis. Future works can study other aspects of the proposed model for a multi-level petroleum product supply chain in different states of certain parameters and time zones.

Practical implications

The designed model can directly and transparently help the oil managers and decision-makers lower the costs of manufacturing, distribution and sales with respect to the determined criteria.

Originality/value

This paper establishes that effectiveness of the dynamic petroleum materials supply chain design will increase by considering maintained and increased production costs and coordinate management flows at all levels by supply chain creation’s integration.

Simulation and Optimization for a Closed-Loop Vessel Dispatching Problem in the Middle East Considering Various Uncertainties

The downstream sectors of the hydrocarbon industry in the Middle East are growing quickly. Due to their geographical locations, they need to transport products from manufacturing plants at one port to other hub ports for international shipping, forming complex closed-loop shipping systems. Such domestic shipping systems are also typical logistics structures in many energy and heavy industries near coastal regions. The operations in such systems are frequently lagging due to uncertainties, such as weather and unexpected events, and the lack of effective management techniques. More reliable and efficient systems require a better vessel operations management policy than one based on a first-available-first-use policy and constant voyage speed. This study develops a detailed and realistic simulation model to evaluate the economic and environmental performance of a closed-loop vessel shipping system, considering various uncertainties from weather and port operations. Furthermore, the optimization model has been incorporated into the simulation model to prescribe the optimal number of vessels and voyage speed to minimize the total costs. A new vessel dispatching policy, large-vessel-first-use, has been proposed and compared with the first-available-first-use policy using the developed model. Increased use of large vessels and slower voyage speeds significantly benefited the total costs and environmental effects. The optimal solution presented the potential to save 26.8% of the total cost and reduce greenhouse gas emissions up to 39% compared with the current operating condition.

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.